Unsupervised Learning

Unsupervised learning is where you only have input data (X) and no corresponding output variables. The goal for unsupervised learning is to model the underlying structure or distribution in the data in order to learn more about the data. These are called unsupervised learning because unlike supervised learning above there is no correct answers and there is no teacher. Algorithms are left to their own devises to discover and present the interesting structure in the data. Unsupervised learning problems can be further grouped into clustering and association problems.

Clustering: A clustering problem is where you want to discover the inherent groupings in the data, such as grouping customers by purchasing behavior.

Clustering is a Machine Learning technique that involves the grouping of data points. Given a set of data points, we can use a clustering algorithm
to classify each data point into a specific group. In theory, data points that are in the same group should have similar properties and/or features,
while data points in different groups should have highly dissimilar properties and/or features. Clustering is a method of unsupervised learning
and is a common technique for statistical data analysis used in many fields.

Association: An association rule learning problem is where you want to discover rules that describe large portions of your data, such as people that buy X also tend to buy Y.

Association rule learning is a rule-based machine learning method for discovering interesting relations between variables in large databases.
It is intended to identify strong rules discovered in databases using some measures of interestingness. This rule-based approach also generates new rules as it analyzes more data.
The ultimate goal, assuming a large enough dataset, is to help a machine mimic the human brain’s feature extraction and abstract association capabilities from new uncategorized data.